The statements in this section merely provide background information related to the disclosure and may not constitute prior art.
Healthcare delivery institutions or environments, such as hospitals or clinics, include information systems, such as hospital information systems (HIS), radiology information systems (RIS), clinical information systems (CIS), and cardiovascular information systems (CVIS), and storage systems, such as picture archiving and communication systems (PACS), library information systems (LIS), and electronic medical records (EMR). Information stored can include patient medication orders, medical histories, imaging data, test results, diagnosis information, management information, and/or scheduling information, for example.
Healthcare delivery institutions are business systems that can be designed and operated to achieve their stated missions robustly. As is the case with other business systems such as those designed to provide services and manufactured goods, there are benefits to reducing variation such that the stake-holders within these business systems can focus more fully on the value added core processes that achieve the stated mission and less on activity responding to needless variations such as delays, accelerations, backups, underutilized assets, unplanned overtime by staff and stock outs of material, equipment, people and space that were preventable and/or scheduled for. In this way the system can achieve its mission more reliably and be robust to exogenous forces outside of the process control.
Currently clinical process decisions have historically relied on the art of understanding symptoms and diagnosing causality much in alignment with the practice of the medical diagnosis arts. In an ever-evolving environment, judgment and experientially-developed mental models are utilized by the healthcare providers to utilize the information currently at hand to make decisions. Presented with similar data, the decision made from one maker to another typically exhibits a large variation. Presented with partial information, which is the byproduct of being organized in functional departments, specialties, roles and by the nature of having partial and/or current or dated information availability on hand—clinical process decisions vary widely and typically are locally focused for lack of a systems view upstream and downstream of the decision point.
Where information systems exist, they are simply informational in nature. Examples include scheduled rooms, people, materials and equipment. Recent advances in locating devices such as those utilizing radio-frequency identification (RFID) technology to report a location of a tagged asset are utilized, yet personnel are loath to be tracked by wearing a device. RFID devices are not pervasive, and the systems that monitor them are not fully integrated with the requisite adjacent systems that gather contextual information. The current art is not predictive, probabilistic nor necessarily systemic. For example, knowing the location of an asset is desirable but knowing its anticipated need and interdependencies is required to make a decision to use a located asset actionable. The information required for such a decision comes from a plurality of adjacent health information systems and must have an ability to play forward into the future.
In the current art, current procedure duration and room status is provided without any proactive or forward-looking capability. In the current art, schedules are produced before a day's activities commence. In the current art, process status is displayed along with trending and, often, alarm functionality should a process variable trip a threshold set point.
There is therefore an unmet need for an integrated system and method for scheduling clinical activities and procedures that incorporate variation, staff and equipment preferences, interdependencies and information flow into the clinical delivery of healthcare that can “look ahead” and enable “what-if” capability for prospective decision support.